On causal discovery with an equal-variance assumption
By Wenyu Chen, Mathias Drton, Y. Wang. Biometrika, 2018
102 citations recorded by the source corpus.
Prior work has shown that causal structure can be uniquely identified from observational data when these follow a structural equation model whose error terms have equal variance. We show that this fact is implied by an ordering among conditional variances. We demonstrate that ordering estimates of these variances yields a simple yet state-of-the-art method for causal structure learning that is readily extendable to high-dimensional problems.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/e0d2fd52618f0b0e6b2e157caaa85445159ae7e1
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In Artificial Intelligence
- Bryon Aragam
- Eric P. Xing
- Pradeep Ravikumar
- Xun Zheng
- Chen Dan
- Yoshua Bengio
- Simon Lacoste-Julien
- Bernhard Schölkopf
In Computational Theory and Mathematics
- Eric P. Xing
- Pradeep Ravikumar
- Bryon Aragam
- Xun Zheng
- Simon Lacoste-Julien
- Yoshua Bengio
- Ignavier Ng
- Shengyu Zhu
In Molecular Biology
Citations in the corpus
- Learning Sparse Nonparametric DAGs (2019, background): “A common approach is to assume the fj are easily parametrized (e.g. linearity) (Zheng et al., 2018; Aragam and Zhou, 2015; Gu et al., 2018; Park and Raskutti, 2017; Park, 2018; Chen et al., 2018; Ghoshal and Honorio, 2017).”